Reducing scan time in (177)Lu planar scintigraphy using convolutional neural network: A Monte Carlo simulation study

PURPOSE: The aim of this study was to reduce scan time in (177)Lu planar scintigraphy through the use of convolutional neural network (CNN) to facilitate personalized dosimetry for (177)Lu‐based peptide receptor radionuclide therapy. METHODS: The CNN model used in this work was based on DenseNet, and the training and testing datasets were generated from Monte Carlo simulation. The CNN input images (IMG(input)) consisted of (177)Lu planar scintigraphy that contained 10–90% of the total photon counts, while the corresponding full‐count images (IMG(100%)) were used as the CNN label images. Two‐sample t‐test was conducted to compare the difference in pixel intensities within region of interest between IMG(100%) and CNN output images (IMG(output)). RESULTS: No difference was found in IMG(output) for rods with diameters ranging from 13 to 33 mm in the Derenzo phantom with a target‐to‐background ratio of 20:1, while statistically significant differences were found in IMG(output) for the 10‐mm diameter rods when IMG(input) containing 10% to 60% of the total photon counts were denoised. Statistically significant differences were found in IMG(output) for both right and left kidneys in the NCAT phantom when IMG(input) containing 10% of the total photon counts were denoised. No statistically significant differences were found in IMG(output) for any other source organs in the NCAT phantom. CONCLUSION: Our results showed that the proposed method can reduce scan time by up to 70% for objects larger than 13 mm, making it a useful tool for personalized dosimetry in (177)Lu‐based peptide receptor radionuclide therapy in clinical practice.